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双胞胎神经成像分析有助于改善患有自闭症谱系障碍的年轻人的分类

Ali Jahani¹, Iman Jahani¹, Ali Khadem²

¹Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Scientific reports

|August 29, 2024

概括

这项研究表明,结合结构性MRI和功能性MRI数据,可以准确地对年轻人进行自闭症谱系障碍 (ASD) 的分类. 通过深度学习,ALFF-sMRI模型实现了最高的诊断准确性.

科学领域:

神经成像是一种神经成像.
机器学习机器学习
发育神经科学的发展神经科学.

背景情况:

自闭症谱系障碍 (ASD) 诊断依赖于行为评估.
神经成像提供了补充数据,但诊断应用有限.
为了客观地分类ASD,研究先进的神经成像技术至关重要.

研究的目的:

评估多模态磁共振成像 (MRI) 对比的诊断实用性,以对年轻人的自闭症谱系障碍 (ASD) 进行分类.
评估深度学习模型的性能,使用单个和组合的MRI数据进行ASD分类.
为了比较不同MRI数据组合的分类准确性.

主要方法:

利用自闭症脑成像数据交换 (ABIDE) 数据集与702名参与者 (351名ASD,351名对照,年龄2-30岁).
使用3D-DenseNet深度学习网络,提供单通道 (sMRI,ALFF,fALFF) 和双通道 (ALFF-sMRI,fALFF-sMRI) 的输入.
进行了对模型培训和测试的十倍交叉验证.

主要成果:

所有测试的模型都实现了超过65.1%的分类准确度.
双通道ALFF-sMRI模型显示了最高的平均精度,为76.9%±2.34%.
一个基于ALFF的单通道模型实现了72%±3.1%的平均精度.

结论:

深度学习模型可以有效地使用各种神经成像输入来分类ASD.
结合结构和功能MRI数据,显著提高了分类准确性.
ALFF-sMRI双通道模型显示出ASD分类的卓越性能,具有潜在的临床实用性.

关键词:

人工智能的人工智能是人工智能.自闭症谱系障碍自闭症谱系障碍深度学习是一种深度学习.多模式学习是多模式学习.休息状态的功能性MRI.结构性核磁共振 (MRI) 是一种结构性核磁共振.

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